The need for more cost-effective, flexible, and convenient high-performance computing is a common thread across diverse areas of Bioinformatics and Computational Biology. We address this problem by enabling the use of a new generation of computers, based on configurable circuits or FPGAs, that is capable of delivering 100-fold speed-ups per node over current microprocessor-based systems. The primary technology to be developed involves creating FGPA-accelerated versions of production applications from three key domains: molecular dynamics, sequence alignment, and docking. The primary aims are for these applications to run on cost-effective, readily available, platforms;scale to large systems;be validated and transparent to the end user;be economically maintainable with respect to changes in hardware, software, and algorithm;and be delivered in stages, with useful artifacts being produced early in the grant period and then at regular intervals thereafter. Important to these goals, especially maintainability, is the continued development of supporting design infrastructure. The potential significance lies in higher throughput of current tasks, higher quality of results, wider use of high performance computing, or the capability of addressing more ambitious problems altogether. The innovation of the proposed work falls into two categories: the methods used in application development, including the design tools created to execute those methods;and the per application, FPGA-aware, restructurings and algorithms. A key issue addressed in the former is the division of labor among application specialist and FPGA/logic designer that is required to create highly efficient, production-quality, life science applications. The relevance to public health is in improving the productivity of the life science researcher: enhanced computing capability leads to more hypotheses explored in finer detail, or the processing of more complex phenomena within a given turnaround time. For example, this would allow multiple parts of a cell to be modeled together, rather than one at a time, resulting in increased understanding of disease processes with implications for improved drug design.